Skip to content

Latest commit

 

History

History
101 lines (83 loc) · 2.63 KB

README.md

File metadata and controls

101 lines (83 loc) · 2.63 KB
Raw

Garbage_Classification

image

Description

[1]https://www.bilibili.com/video/BV1gz41187Hk [2]https://www.bilibili.com/video/BV1MT4y1V74w

We also provide PyTorch code for deployment on mobile device.

Tree

.
├── bin
│   ├── app_test.py
│   ├── demo.py
│   ├── my_test.py
│   └── my_train.py
├── data
├── gcnet
│   ├── classifier.py
│   ├── __init__.py
│   ├── json_utils.py
│   ├── logger.py
│   ├── __pycache__
│   ├── resnet.py
│   ├── test.py
│   ├── train.py
│   ├── transforms.py
│   └── utils.py
├── models
└── preprocess
    ├── 01.html
    ├── 01-原始数据集分布可视化分析.py
    ├── 02-原始数据集train-val划分.py
    ├── 03.html
    ├── 03-train和val数据分布可视化.py
    ├── 04.html
    ├── 04-四大类垃圾分布可视化.py
    ├── 05.html
    ├── 05-四大类垃圾train-val分布可视化.py
    ├── 06-数据增强transform.py
    ├── 07-原始数据可视化.py
    ├── 08-预处理数据加载.py
    ├── 09-测试resnext101模型.py
    ├── 10-Web服务环境搭建.py
    ├── 11-分类网络环境搭建.py
    └── images

Demo

python ./demo.py

Train

You should download data and models from BaiduYun: https://pan.baidu.com/s/1g9RoIGxf2OD1zo4bgbMQWg password: cdz5

python ./my_train.py

Test

python ./my_test.py

Web

python ./app_test.py

Experiment

Model Iter precision recall f1-score
resnext101_32x16d 10 0.9827 0.9826 0.9826
resnext101_32x8d 30 0.9589 0.9588 0.9583
resnext101_32x8d 10 0.9473 0.9472 0.9472
resnet18 10 0.8968 0.8959 0.8940
  • resnext101_32x16d
LR epoch Train Loss Valid Loss Train Acc. Valid Acc.
0.001000 1.000000 0.281912 0.241702 90.296428 91.276075
0.001000 2.000000 0.177333 0.147571 93.530952 94.628832
0.001000 3.000000 0.163498 0.135344 94.181235 95.118656
0.001000 4.000000 0.148997 0.081726 94.586606 96.968161
0.001000 5.000000 0.133063 0.090255 95.110210 96.807702
0.001000 6.000000 0.125995 0.069795 95.346677 97.415759
0.001000 7.000000 0.122259 0.102625 95.642260 96.351659
0.001000 8.000000 0.127478 0.068116 95.422684 97.660671
0.001000 9.000000 0.132976 0.053337 95.312896 98.268727
0.001000 10.000000 0.118554 0.068123 95.616924 97.669116